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reglogit (version 1.2-8)

Simulation-Based Regularized Logistic Regression

Description

Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 ).

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Version

Install

install.packages('reglogit')

Monthly Downloads

293

Version

1.2-8

License

LGPL

Maintainer

Robert Gramacy

Last Published

July 24th, 2025

Functions in reglogit (1.2-8)

predict.reglogit

Prediction for regularized (polychotomous) logistic regression models
pima

Pima Indian Data
reglogit-internal

Internal reglogit Functions
reglogit

Gibbs sampling for regularized logistic regression